HarpethTMDL
RJ
2022-08-09
WASP.version<-8Introduction
Harpeth TMDL – Water Quality Analysis Simulation Program (WASP version 8)
Exploration of water quality data using the calibrated WASP models provided to TDEC and others by the EPA Region 4
This file was last updated on 2022-08-09 11:50:26
#Import Data
Grab <- read_excel("GrabSamples.xlsx",
col_types = c("skip", "text", "text",
"skip", "skip", "date", "text", "numeric",
"text"))
head(Grab,15)
## # A tibble: 15 × 6
## DataSource Station_ID Date_Time Pcode Result Units
## <chr> <chr> <dttm> <chr> <dbl> <chr>
## 1 STORET 11NPSWRD_WQX_NATR_BUBR 2012-01-03 08:33:00 COND 306 uS/c…
## 2 STORET 11NPSWRD_WQX_NATR_BUBR 2012-01-03 08:33:00 DOSAT 90.3 %
## 3 STORET 11NPSWRD_WQX_NATR_BUBR 2012-01-03 08:33:00 DO 12.6 mg/l
## 4 STORET 11NPSWRD_WQX_NATR_BUBR 2012-01-03 08:33:00 PH 7.76 std …
## 5 STORET 11NPSWRD_WQX_NATR_BUBR 2012-01-03 08:33:00 NO3_N 3.03 mg/l
## 6 STORET 11NPSWRD_WQX_NATR_BUBR 2012-01-03 08:33:00 TEMPWC 0.4 degC
## 7 STORET 11NPSWRD_WQX_NATR_BUBR 2012-01-03 08:33:00 FLOW_CMS 0.0212 cms
## 8 TDEC TNW000002791 2012-01-03 11:55:00 COND 376 uS/c…
## 9 TDEC TNW000002790 2012-01-03 10:45:00 COND 373 uS/c…
## 10 TDEC TNW000003300 2012-01-03 09:35:00 COND 295 uS/c…
## 11 TDEC TNW000005654 2012-01-03 12:20:00 COND 246 uS/c…
## 12 TDEC TNW000003508 2012-01-03 11:15:00 COND 242 uS/c…
## 13 TDEC TNW000006704 2012-01-03 13:15:00 COND 234 uS/c…
## 14 TDEC TNW000006704 2012-01-03 13:15:00 DO 16.5 mg/l
## 15 TDEC TNW000003508 2012-01-03 11:15:00 DO 15.6 mg/l
BaseModel <- read_delim("Epa_Base_Model_2011_To_2020.txt",
delim = "\t", escape_double = FALSE,
col_types = cols(`Date-Time` = col_datetime(format = "%m/%d/%Y %H:%M"),
CBOD = col_number(), DO = col_number(),
FLOW_CMS = col_number(), NH3_N = col_number(),
NO3O2 = col_number(), PHYTO = col_number(),
SOD_T = col_number(), `SOLID-WS` = col_number(),
TN = col_number(), TP = col_number(),
WTEMP = col_number()), trim_ws = TRUE)
head(BaseModel,15)
## # A tibble: 15 × 13
## `Date-Time` Station_ID CBOD DO FLOW_…¹ NH3_N NO3O2 PHYTO SOD_T
## <dttm> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 ARKANSAS_131 0 0 0.202 0 0 0 0
## 2 2011-01-02 00:04:00 ARKANSAS_131 0.742 7.22 0.200 0.0116 0.153 0 1.22
## 3 2011-01-03 00:01:00 ARKANSAS_131 0.742 10.7 0.200 0.0223 0.158 0 1.26
## 4 2011-01-04 00:04:00 ARKANSAS_131 0.742 10.7 0.200 0.0354 0.160 0 1.25
## 5 2011-01-05 00:08:00 ARKANSAS_131 0.742 10.7 0.200 0.0465 0.161 0 1.24
## 6 2011-01-06 00:01:00 ARKANSAS_131 0.742 10.7 0.200 0.0550 0.161 0 1.22
## 7 2011-01-07 00:04:00 ARKANSAS_131 0.742 10.7 0.200 0.0612 0.161 0 1.21
## 8 2011-01-08 00:08:00 ARKANSAS_131 0.742 10.7 0.200 0.0658 0.161 0 1.20
## 9 2011-01-09 00:01:00 ARKANSAS_131 0.742 10.7 0.200 0.0690 0.161 0 1.19
## 10 2011-01-10 00:04:00 ARKANSAS_131 0.742 10.7 0.200 0.0711 0.161 0 1.18
## 11 2011-01-11 00:07:00 ARKANSAS_131 0.742 10.7 0.200 0.0724 0.161 0 1.18
## 12 2011-01-12 00:01:00 ARKANSAS_131 0.742 10.7 0.200 0.0731 0.161 0 1.17
## 13 2011-01-13 00:04:00 ARKANSAS_131 0.742 10.7 0.200 0.0733 0.161 0 1.16
## 14 2011-01-14 00:07:00 ARKANSAS_131 0.742 10.7 0.200 0.0731 0.161 0 1.15
## 15 2011-01-15 00:00:00 ARKANSAS_131 0.742 10.7 0.200 0.0727 0.161 0 1.15
## # … with 4 more variables: `SOLID-WS` <dbl>, TN <dbl>, TP <dbl>, WTEMP <dbl>,
## # and abbreviated variable name ¹​FLOW_CMS
## # ℹ Use `colnames()` to see all variable names
Cont <- read_csv("Cont.csv", col_types = cols(...1 = col_skip(),
Date_Time = col_datetime(format = "%Y-%m-%d %H:%M:%S"),
Result = col_number()))
head(Cont,15)
## # A tibble: 15 × 5
## Station_ID Date_Time Pcode Result Units
## <chr> <dttm> <chr> <dbl> <chr>
## 1 USGS_03432390 2012-01-26 00:00:00 TEMPWC 11.8 degC
## 2 USGS_03432390 2012-01-26 00:00:00 COND 550 <NA>
## 3 USGS_03432390 2012-01-26 06:00:00 TEMPWC 12.6 degC
## 4 USGS_03432390 2012-01-26 06:00:00 COND 552 <NA>
## 5 USGS_03432390 2012-01-26 12:00:00 TEMPWC 13.6 degC
## 6 USGS_03432390 2012-01-26 12:00:00 COND 499 <NA>
## 7 USGS_03432390 2012-01-26 18:00:00 TEMPWC 14 degC
## 8 USGS_03432390 2012-01-26 18:00:00 COND 451 <NA>
## 9 USGS_03432390 2012-01-27 00:00:00 TEMPWC 12.5 degC
## 10 USGS_03432390 2012-01-27 00:00:00 COND 306 <NA>
## 11 USGS_03432390 2012-01-27 06:00:00 TEMPWC 11.9 degC
## 12 USGS_03432390 2012-01-27 06:00:00 COND 455 <NA>
## 13 USGS_03432390 2012-01-27 12:00:00 TEMPWC 12.2 degC
## 14 USGS_03432390 2012-01-27 12:00:00 COND 512 <NA>
## 15 USGS_03432390 2012-01-27 18:00:00 TEMPWC 12.6 degC
Natural <- read_delim("NATURAL_MODEL_2011_To_2020.txt",
delim = "\t", escape_double = FALSE,
col_types = cols(`Date-Time` = col_datetime(format = "%m/%d/%Y %H:%M"),
CBOD90 = col_number(), DIP = col_number(),
DO = col_number(), DON = col_number(),
DOP = col_number(), FLOW_CMS = col_number(),
NH3_N = col_number(), NO3O2 = col_number(),
PHYTO = col_number(), PON = col_number(),
SOD_T = col_number(), TKN = col_number(),
TN = col_number(), TP = col_number(),
WTEMP = col_number()), trim_ws = TRUE)
head(Natural,15)
## # A tibble: 15 × 17
## `Date-Time` Stati…¹ CBOD90 DIP DO DON DOP FLOW_…² NH3_N
## <dttm> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 ARKANS… 0 0 0 0 0 0.210 0
## 2 2011-01-02 00:06:00 ARKANS… 2.38 0.0968 7.18 0.0193 0.0107 0.208 0.00973
## 3 2011-01-03 00:04:00 ARKANS… 2.38 0.0948 10.7 0.0193 0.0107 0.208 0.0200
## 4 2011-01-04 00:02:00 ARKANS… 2.38 0.0949 10.6 0.0193 0.0107 0.208 0.0323
## 5 2011-01-04 23:59:00 ARKANS… 2.38 0.0951 10.6 0.0193 0.0107 0.208 0.0427
## 6 2011-01-06 00:05:00 ARKANS… 2.38 0.0953 10.6 0.0193 0.0107 0.208 0.0505
## 7 2011-01-07 00:02:00 ARKANS… 2.38 0.0954 10.6 0.0193 0.0107 0.208 0.0562
## 8 2011-01-08 00:00:00 ARKANS… 2.38 0.0956 10.6 0.0193 0.0107 0.208 0.0603
## 9 2011-01-09 00:06:00 ARKANS… 2.38 0.0957 10.6 0.0193 0.0107 0.208 0.0631
## 10 2011-01-10 00:03:00 ARKANS… 2.38 0.0958 10.6 0.0193 0.0107 0.208 0.0649
## 11 2011-01-11 00:00:00 ARKANS… 2.38 0.0960 10.6 0.0193 0.0107 0.208 0.066
## 12 2011-01-12 00:06:00 ARKANS… 2.38 0.0961 10.6 0.0193 0.0107 0.208 0.0665
## 13 2011-01-13 00:04:00 ARKANS… 2.38 0.0962 10.6 0.0193 0.0107 0.208 0.0666
## 14 2011-01-14 00:01:00 ARKANS… 2.38 0.0963 10.6 0.0193 0.0107 0.208 0.0662
## 15 2011-01-14 23:58:00 ARKANS… 2.38 0.0964 10.6 0.0193 0.0107 0.208 0.0657
## # … with 8 more variables: NO3O2 <dbl>, PHYTO <dbl>, PON <dbl>, SOD_T <dbl>,
## # TKN <dbl>, TN <dbl>, TP <dbl>, WTEMP <dbl>, and abbreviated variable names
## # ¹​Station_ID, ²​FLOW_CMS
## # ℹ Use `colnames()` to see all variable names
Base_PS <- read_delim("Harpeth_Epa_Base_Model_2_25_X_Ps.txt",
delim = "\t", escape_double = FALSE,
col_types = cols(`Date-Time` = col_datetime(format = "%m/%d/%Y %H:%M"),
Station_ID = col_character(), CBOD = col_number(),
DIP = col_number(), DO = col_number(),
DON = col_number(), DOP = col_number(),
FLOW_CMS = col_number(), NH3_N = col_number(),
NO3O2 = col_number(), PHYTO = col_number(),
PON = col_number(), SOD_T = col_number(),
TN = col_number(), TP = col_number(),
WTEMP = col_number()), trim_ws = TRUE)
head(Base_PS)
## # A tibble: 6 × 16
## `Date-Time` Station_ID CBOD DIP DO DON DOP FLOW_…¹ NH3_N
## <dttm> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2013-01-01 00:00:00 ARKANSAS_1… 0 0 0 0 0 0.0421 0
## 2 2013-01-01 06:03:00 ARKANSAS_1… 1.87 0.431 5.07 0.114 0.0479 0.0434 0.0576
## 3 2013-01-01 12:03:00 ARKANSAS_1… 2.13 0.447 5.64 0.125 0.0499 0.0455 0.0625
## 4 2013-01-01 18:00:00 ARKANSAS_1… 1.97 0.381 5.71 0.111 0.0426 0.0453 0.0555
## 5 2013-01-01 23:59:00 ARKANSAS_1… 1.88 0.352 5.82 0.103 0.0393 0.0448 0.0516
## 6 2013-01-02 06:03:00 ARKANSAS_1… 1.80 0.290 5.63 0.0959 0.0367 0.0443 0.0982
## # … with 7 more variables: NO3O2 <dbl>, PHYTO <dbl>, PON <dbl>, SOD_T <dbl>,
## # TN <dbl>, TP <dbl>, WTEMP <dbl>, and abbreviated variable name ¹​FLOW_CMS
## # ℹ Use `colnames()` to see all variable names
unique(Grab[c(4,6)])
## # A tibble: 52 × 2
## Pcode Units
## <chr> <chr>
## 1 COND uS/cm @25C
## 2 DOSAT %
## 3 DO mg/l
## 4 PH std units
## 5 NO3_N mg/l
## 6 TEMPWC degC
## 7 FLOW_CMS cms
## 8 TSS mg/l
## 9 TURB NTU
## 10 NO3O2_N mg/l
## # … with 42 more rows
## # ℹ Use `print(n = ...)` to see more rows
library(readr)
Base_NoFrank <- read_delim("Base_Epa_Model_No_Franklin_Stp_Discharge.txt",
delim = "\t", escape_double = FALSE,
col_types = cols(`Date-Time` = col_datetime(format = "%m/%d/%Y %H:%M"),
CBOD = col_number(), DIP = col_number(),
DO = col_number(), DON = col_number(),
DOP = col_number(), FLOW_CMS = col_number(),
NH3_N = col_number(), NO3O2_N = col_number(),
PHYTO = col_number(), PON = col_number(),
SOD_T = col_number(), TN = col_number(),
TP = col_number(), WTEMP = col_number()),
trim_ws = TRUE)
head(Base_NoFrank)
## # A tibble: 6 × 16
## `Date-Time` Station_ID CBOD DIP DO DON DOP FLOW_…¹ NH3_N
## <dttm> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2011-01-01 00:00:00 ARKANSAS_1… 0 0 0 0 0 0.210 0
## 2 2011-01-02 00:04:00 ARKANSAS_1… 0.742 0.104 5.42 0.0231 0.0115 0.204 0.0116
## 3 2011-01-02 23:58:00 ARKANSAS_1… 0.742 0.104 5.35 0.0231 0.0115 0.204 0.0646
## 4 2011-01-04 00:01:00 ARKANSAS_1… 0.742 0.104 10.7 0.0231 0.0115 0.204 0.0633
## 5 2011-01-05 00:05:00 ARKANSAS_1… 0.742 0.104 10.7 0.0231 0.0115 0.204 0.0621
## 6 2011-01-05 23:58:00 ARKANSAS_1… 0.742 0.104 10.7 0.0231 0.0115 0.204 0.0610
## # … with 7 more variables: NO3O2_N <dbl>, PHYTO <dbl>, PON <dbl>, SOD_T <dbl>,
## # TN <dbl>, TP <dbl>, WTEMP <dbl>, and abbreviated variable name ¹​FLOW_CMS
## # ℹ Use `colnames()` to see all variable names
library(readr)
NPDES <- read_delim("Point_Source_Wastestream_Dataset.txt",
delim = "\t", escape_double = FALSE,
col_types = cols(`Date-Time` = col_datetime(format = "%m/%d/%Y %H:%M"),
CBOD = col_number(), DO = col_number(),
FLOW = col_number(), NH3 = col_number(),
NOX = col_number(), `NP RATIO` = col_number(),
ORGN = col_number(), ORGP = col_number(),
PO4 = col_number(), TKN = col_number(),
TN = col_number(), TP = col_number(),
TSS = col_number(), WTEMP = col_number()),
trim_ws = TRUE)
Grab data table contains grab sample results collected
from 171 locations throughout the Harpeth River watershed
#Subset Data into Logical WASP Segments
# HARPETH_1 = Mouth of the Harpeth
# JONES_167 = Jones Creek
# HARPETH_2 = Main Stem w/o Jones Creek
# TURNBULL_143 = Turnbull Creek
# S_HARPETH_124 = South Harpeth
# HARPETH_34 = West Harpeth
# HARPETH_62 = Upstream of Franklin STP
# LSPC188PERO = Downstream of Franklin STP
# LSPC183PERO = Headwaters
# LSPC192PERO = Hwy100
# LSPC197PERO = 70s Bridge
sites=c("HARPETH_1","JONES_167","HARPETH_2", "TURNBULL_143", "S_HARPETH_124", "HARPETH_34", "LSPC188PERO", "HARPETH_62", "LSPC183PERO","LSPC192PERO", "LSPC197PERO")
colnames(BaseModel)[1] <- "Date"
BaseModel$TPLoad<-BaseModel$TP*0.00220462*BaseModel$FLOW_CMS*86400
BaseModel$TPLoad.csum <- ave(BaseModel$TPLoad, BaseModel$Station_ID, FUN=cumsum)
BaseModel$NP<-BaseModel$TN/BaseModel$TP
BaseModel1<-BaseModel
BaseModel<-BaseModel[BaseModel$Station_ID==sites,]
colnames(Grab)[3] <- "Date"
colnames(Cont)[2] <- "Date"
colnames(Natural)[1] <- "Date"
colnames(Base_PS)[1] <- "Date"
colnames(NPDES)[1] <- "Date"
Natural$NP<-Natural$TN/Natural$TP
Natural$TPLoad<-Natural$TP*0.00220462*Natural$FLOW_CMS*86400
Natural$TPLoad.csum <- ave(Natural$TPLoad, Natural$Station_ID, FUN=cumsum)
Natural1<-Natural
Natural<-Natural[Natural$Station_ID==sites,]
Base_PS<-Base_PS[Base_PS$Station_ID==sites,]
Base_PS$TPLoad<-Base_PS$TP*0.00220462*Base_PS$FLOW_CMS*86400
Base_PS1<-Base_PS
colnames(Base_NoFrank)[1] <- "Date"
Base_NoFrank$TPLoad<-Base_NoFrank$TP*0.00220462*Base_NoFrank$FLOW_CMS*86400
Base_NoFrank$TPLoad.csum <- ave(Base_NoFrank$TPLoad, Base_NoFrank$Station_ID, FUN=cumsum)
Base_NoFrank$NP<-Base_NoFrank$TN/Base_NoFrank$TP
Base_NoFrank1<-Base_NoFrank
Base_NoFrank<-Base_NoFrank[Base_NoFrank$Station_ID==sites,]
NPDES$TPLoad<-NPDES$TP*0.00220462*NPDES$FLOW*86400
NPDES$TPLoad.csum <- ave(NPDES$TPLoad, NPDES$Station_ID, FUN=cumsum)
NPDES$NP<-NPDES$TN/NPDES$TP
NPDES1<-NPDES
#####################################################################################################
molten.data <- melt(Grab, id = c("Date","Station_ID","Pcode","Units","DataSource"))
Grab.w<-dcast(molten.data, Date+Station_ID~Pcode, mean)
Grab.w$NP<-Grab.w$TN/Grab.w$TP
head(Grab.w, 10)
## Date Station_ID ALK BOD30 BOD5 CBOD30 CBOD5
## 1 2012-01-03 08:33:00 11NPSWRD_WQX_NATR_BUBR NaN NaN NaN NaN NaN
## 2 2012-01-03 09:35:00 TNW000003300 NaN NaN NaN NaN NaN
## 3 2012-01-03 10:45:00 TNW000002790 NaN NaN NaN NaN 1
## 4 2012-01-03 11:15:00 TNW000003508 NaN NaN NaN NaN NaN
## 5 2012-01-03 11:55:00 TNW000002791 NaN NaN NaN NaN NaN
## 6 2012-01-03 12:20:00 TNW000005654 NaN NaN NaN NaN NaN
## 7 2012-01-03 13:15:00 TNW000006704 NaN NaN NaN NaN NaN
## 8 2012-01-04 08:30:00 TNW000003576 NaN NaN NaN NaN 1
## 9 2012-01-04 09:43:00 TNW000006443 NaN NaN NaN NaN NaN
## 10 2012-01-04 10:25:00 TNW000003306 NaN NaN NaN NaN NaN
## CBOD90 CHLA COND DO DOSAT FLOW_CFS FLOW_CMS NBOD90 NH3_N NO2 NO2_N NO3
## 1 NaN NaN 306 12.55 90.3 NaN 0.02123764 NaN NaN NaN NaN NaN
## 2 NaN NaN 295 14.36 NaN NaN NaN NaN NaN NaN NaN NaN
## 3 NaN NaN 373 12.81 NaN NaN NaN NaN 0.0165 NaN NaN NaN
## 4 NaN NaN 242 15.58 NaN NaN NaN NaN NaN NaN NaN NaN
## 5 NaN NaN 376 13.39 NaN NaN NaN NaN NaN NaN NaN NaN
## 6 NaN NaN 246 15.08 NaN NaN NaN NaN NaN NaN NaN NaN
## 7 NaN NaN 234 16.51 NaN NaN NaN NaN NaN NaN NaN NaN
## 8 NaN NaN 544 13.33 NaN NaN NaN NaN 0.0165 NaN NaN NaN
## 9 NaN NaN 317 14.70 NaN NaN NaN NaN 0.0165 NaN NaN NaN
## 10 NaN NaN 421 17.50 NaN NaN NaN NaN 0.0165 NaN NaN NaN
## NO3_N NO3O2_N OPO4 OPO4_P ORGN ORP PERI%N PERI%P PERI_AI PERI_ASH PERI_NP
## 1 3.03 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
## 2 NaN 0.43 NaN NaN NaN NaN NaN NaN NaN NaN NaN
## 3 NaN 1.10 NaN NaN NaN NaN NaN NaN NaN NaN NaN
## 4 NaN 0.15 NaN NaN NaN NaN NaN NaN NaN NaN NaN
## 5 NaN 1.10 NaN NaN NaN NaN NaN NaN NaN NaN NaN
## 6 NaN 0.41 NaN NaN NaN NaN NaN NaN NaN NaN NaN
## 7 NaN 0.76 NaN NaN NaN NaN NaN NaN NaN NaN NaN
## 8 NaN 1.30 NaN NaN NaN NaN NaN NaN NaN NaN NaN
## 9 NaN 1.70 NaN NaN NaN NaN NaN NaN NaN NaN NaN
## 10 NaN 0.92 NaN NaN NaN NaN NaN NaN NaN NaN NaN
## PERI_TN PERI_TP PERIAFDM PERICHLA PH SOD_20 SOD_T SRP TBOD90 TDP TDS
## 1 NaN NaN NaN NaN 7.76 NaN NaN NaN NaN NaN NaN
## 2 NaN NaN NaN NaN 7.85 NaN NaN NaN NaN NaN NaN
## 3 NaN NaN NaN NaN 8.04 NaN NaN NaN NaN NaN NaN
## 4 NaN NaN NaN NaN 8.27 NaN NaN NaN NaN NaN NaN
## 5 NaN NaN NaN NaN 8.13 NaN NaN NaN NaN NaN NaN
## 6 NaN NaN NaN NaN 8.27 NaN NaN NaN NaN NaN NaN
## 7 NaN NaN NaN NaN 8.45 NaN NaN NaN NaN NaN NaN
## 8 NaN NaN NaN NaN 7.41 NaN NaN NaN NaN NaN NaN
## 9 NaN NaN NaN NaN 8.31 NaN NaN NaN NaN NaN NaN
## 10 NaN NaN NaN NaN 8.59 NaN NaN NaN NaN NaN NaN
## TEMPWC TKN TN TP TS TSS TURB NP
## 1 0.40 NaN NaN NaN NaN NaN NaN NaN
## 2 4.33 0.065 NaN 0.006 NaN 5 0.68 NaN
## 3 6.16 0.065 NaN 0.210 NaN 5 3.07 NaN
## 4 2.61 0.065 NaN 0.013 NaN 5 0.32 NaN
## 5 6.36 0.200 NaN 0.210 NaN 5 2.81 NaN
## 6 6.19 0.065 NaN 0.044 NaN 5 0.74 NaN
## 7 3.39 0.065 NaN 0.082 NaN 5 1.75 NaN
## 8 4.62 0.065 NaN 0.230 NaN 5 1.36 NaN
## 9 4.60 0.065 NaN 0.210 NaN 5 0.52 NaN
## 10 5.46 0.200 NaN 0.033 NaN 5 0.88 NaN
molten.cont <- melt(Cont, id = c("Date","Station_ID","Pcode","Units"))
Cont.w<-dcast(molten.cont, Date+Station_ID~Pcode, mean)
head(Cont.w, 10)
## Date Station_ID COND DO DOSAT FLOW_CFS FLOW_CMS PH TEMPWC TURB
## 1 2011-01-01 USGS_03432350 NaN NaN NaN 828.00 23.44634932 NaN NaN NaN
## 2 2011-01-01 USGS_03432400 NaN NaN NaN 919.00 26.02318239 NaN NaN NaN
## 3 2011-01-01 USGS_03433500 NaN NaN NaN 456.00 12.91248223 NaN NaN NaN
## 4 2011-01-01 USGS_03433640 NaN NaN NaN 3.83 0.10845352 NaN NaN NaN
## 5 2011-01-01 USGS_03434500 NaN NaN NaN 464.00 13.13901701 NaN NaN NaN
## 6 2011-01-02 USGS_03432350 NaN NaN NaN 1020.00 28.88318394 NaN NaN NaN
## 7 2011-01-02 USGS_03432400 NaN NaN NaN 1330.00 37.66140651 NaN NaN NaN
## 8 2011-01-02 USGS_03433500 NaN NaN NaN 2220.00 62.86340034 NaN NaN NaN
## 9 2011-01-02 USGS_03433640 NaN NaN NaN 1.44 0.04077626 NaN NaN NaN
## 10 2011-01-02 USGS_03434500 NaN NaN NaN 2130.00 60.31488411 NaN NaN NaN#Data Summary
Subset of 11 modeled segments in WASP
#ggplot(data = BaseModel, aes(x = Date, y = FLOW_CMS, group = Station_ID, colour = Station_ID)) +
# geom_line() +
# facet_wrap(~ Station_ID)
#ggplot(data = BaseModel, aes(x = Date, y = TPLoad, group = Station_ID, colour = Station_ID, )) +
# ylab("TP Load (lbs/day)") +
# geom_line() +
# facet_wrap(~ Station_ID)
#Boxplot - look at data distribution
tp<-ggplot(BaseModel, aes(x = Station_ID, y = TP, fill = Station_ID)) +
geom_boxplot(outlier.colour="black", outlier.shape=16, outlier.size=2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
ylab("TP (mg/L)")
tp
tn<-ggplot(BaseModel, aes(x = Station_ID, y = TN, fill = Station_ID)) +
geom_boxplot(outlier.colour="black", outlier.shape=16, outlier.size=2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
ylab("TN (mg/L)")
tn
np<-ggplot(BaseModel, aes(x = Station_ID, y = NP, fill = Station_ID)) +
geom_boxplot(outlier.colour="black", outlier.shape=16, outlier.size=2) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))+
ylab("TN:TP Ratio")
np
LSPC188_tp<-ggplot(BaseModel,aes(x=Date,y=TP, fill="Base Model"))+geom_line(data=subset(BaseModel,Station_ID=="LSPC188PERO")) +theme_bw()+
labs(y="TP (mg/L)")+ylim(min(BaseModel$TP),max(BaseModel$TP))+
theme(
axis.text.y=element_text(size=11),axis.title.y=element_text(size=12, vjust=1.2),
axis.text.x=element_blank(),axis.title.x=element_blank(),
panel.border = element_rect(size=.8, colour = "black"))+
geom_point(data=subset(Grab.w, Station_ID==c("Frank_Down","Frank_CottonLn","Franklin_Site_2","Franklin_Site_3")), aes(x=Date, y=TP, color=Station_ID))+
geom_line(data=subset(Natural, Station_ID=="LSPC188PERO"), aes(x=Date, y=TP, color="Natural"))
LSPC188_tp
LSPC188_tn<-ggplot(BaseModel,aes(x=Date,y=TN))+geom_line(data=subset(BaseModel,Station_ID=="LSPC188PERO")) +theme_bw()+
labs(y="TN (mg/L)")+ylim(min(BaseModel$TN),max(BaseModel$TN))+
theme(
axis.text.y=element_text(size=11),axis.title.y=element_text(size=12, vjust=1.2),
axis.text.x=element_blank(),axis.title.x=element_blank(),
panel.border = element_rect(size=.8, colour = "black"))+
geom_point(data=subset(Grab.w, Station_ID==c("Frank_Down","Frank_CottonLn","Franklin_Site_2","Franklin_Site_3")), aes(x=Date, y=TN, color=Station_ID))+
geom_line(data=subset(Natural, Station_ID=="LSPC188PERO"), aes(x=Date, y=TN, color="Natural"))
LSPC188_tn
LSPC188_flow<-ggplot(BaseModel,aes(x=Date,y=FLOW_CMS))+geom_line(data=subset(BaseModel,Station_ID=="LSPC188PERO")) +theme_bw()+
labs(y="Flow (cms)")+ylim(min(BaseModel$FLOW_CMS),200)+
theme(
axis.text.y=element_text(size=11),axis.title.y=element_text(size=12, vjust=1.2),
axis.text.x=element_blank(),axis.title.x=element_blank(),
panel.border = element_rect(size=.8, colour = "black"))+
geom_point(data=subset(Cont.w, Station_ID==c("USGS_03432400")), aes(x=Date, y=FLOW_CMS, color=Station_ID))+
geom_line(data=subset(Natural, Station_ID=="LSPC188PERO"), aes(x=Date, y=FLOW_CMS, color="Natural"))
LSPC188_flow
LSPC188_np<-ggplot(BaseModel,aes(x=Date,y=NP))+geom_line(data=subset(BaseModel,Station_ID=="LSPC188PERO")) +theme_bw()+
labs(y="TN:TP ratio", x="Date")+ylim(min(BaseModel$NP),max(BaseModel$NP))+
theme(
axis.text.y=element_text(size=11),axis.title.y=element_text(size=12, vjust=1.2),
axis.text.x=element_text(size=11),axis.title.x=element_text(size=11),
panel.border = element_rect(size=.8, colour = "black"))+
geom_point(data=subset(Grab.w, Station_ID==c("Frank_Down","Frank_CottonLn","Franklin_Site_2","Franklin_Site_3")), aes(x=Date, y=NP, color=Station_ID))+
geom_line(data=subset(Natural, Station_ID=="LSPC188PERO"), aes(x=Date, y=NP, color="Natural"))
LSPC188_np
grid.arrange(LSPC188_tp,LSPC188_tn,LSPC188_flow,LSPC188_np, ncol=1,padding=100)
HARPETH_62<-ggplot(BaseModel,aes(x=Date,y=TP))+geom_line(data=subset(BaseModel,Station_ID=="HARPETH_62")) +theme_bw()+
labs(y="TP (mg/L)")+ylim(min(BaseModel$TP),max(BaseModel$TP))+
theme(
axis.text.y=element_text(size=16),axis.title.y=element_text(size=12, vjust=1.2),
axis.text.x=element_blank(),axis.title.x=element_blank(),
panel.border = element_rect(size=.8, colour = "black"))+
geom_point(data=subset(Grab.w, Station_ID==c("Frank_Up","Frank_Eff","Franklin_Site_1")), aes(x=Date, y=TP, color=Station_ID))
#HARPETH_62
#ppi=300
#png("awesome stacked time series.png",width=12*ppi, height=8*ppi, res=ppi)
#grid.arrange(LSPC188_tp,HARPETH_62, ncol=1)
#dev.off()
LSPC188_DO<-ggplot(BaseModel,aes(x=Date,y=DO, fill="Base Model"))+geom_line(data=subset(BaseModel,Station_ID=="LSPC188PERO")) +theme_bw()+
labs(y="DO (mg/L)")+ylim(min(BaseModel$DO),max(BaseModel$DO))+
theme(
axis.text.y=element_text(size=11),axis.title.y=element_text(size=12, vjust=1.2),
axis.text.x=element_text(size=11),axis.title.x=element_text(size=11),
panel.border = element_rect(size=.8, colour = "black"))+
geom_point(data=subset(Grab.w, Station_ID==c("Frank_Down","Frank_CottonLn","Franklin_Site_2","Franklin_Site_3")), aes(x=Date, y=DO, color=Station_ID))+
geom_line(data=subset(Natural, Station_ID=="LSPC188PERO"), aes(x=Date, y=DO, colour="Natural"))
LSPC188_DO
LSPC188_DO_PSinc<-ggplot(Base_PS,aes(x=Date,y=DO,fill="Base Model x 2.25 PS"))+geom_line(data=subset(Base_PS,Station_ID=="LSPC188PERO")) +theme_bw()+
labs(y="DO (mg/L)")+ylim(min(Base_PS$DO),max(Base_PS$DO))+
theme(
axis.text.y=element_text(size=11),axis.title.y=element_text(size=12, vjust=1.2),
axis.text.x=element_text(size=11),axis.title.x=element_text(size=11),
panel.border = element_rect(size=.8, colour = "black"))+
geom_point(data=subset(Grab.w, Station_ID==c("Frank_Down","Frank_CottonLn","Franklin_Site_2","Franklin_Site_3")), aes(x=Date, y=DO, colour=Station_ID))+
geom_line(data=subset(Natural, Station_ID=="LSPC188PERO"), aes(x=Date, y=DO, color="Natural"))
LSPC188_DO_PSinc
LSPC188_flow<-ggplot(BaseModel,aes(x=Date,y=FLOW_CMS))+geom_line(data=subset(BaseModel,Station_ID=="LSPC188PERO")) +theme_bw()+
labs(y="Flow (cms)")+ylim(min(BaseModel$FLOW_CMS),200)+
theme(
axis.text.y=element_text(size=11),axis.title.y=element_text(size=12, vjust=1.2),
axis.text.x=element_text(size=11),axis.title.x=element_text(size=11),
panel.border = element_rect(size=.8, colour = "black"))+
geom_point(data=subset(Cont.w, Station_ID==c("USGS_03432400")), aes(x=Date, y=FLOW_CMS, color=Station_ID))+
geom_line(data=subset(Natural, Station_ID=="LSPC188PERO"), aes(x=Date, y=FLOW_CMS, color="Natural"))
LSPC188_flow
grid.arrange(LSPC188_DO,LSPC188_DO_PSinc,LSPC188_flow, ncol=1,padding=100)
p<-ggplot(BaseModel, aes(x=Date, y=DO, fill=Station_ID)) + ggtitle("Base Model/Current Conditions") + theme_bw() +
geom_line(aes(colour=Station_ID, group=Station_ID)) #+
#geom_point(data=subset(Cont.w, Station_ID==c("USGS_03434500","HARPE062")) , aes(x=Date, y=DO))
p
p1<-ggplot(Natural, aes(x=Date, y=DO, fill=Station_ID)) + ggtitle("Natural Conditions") + theme_bw() +
geom_line(aes(colour=Station_ID, group=Station_ID))
p1
p3<-ggplot(Base_PS, aes(x=Date, y=DO, fill=Station_ID)) + theme_bw() +
geom_line(aes(colour=Station_ID, group=Station_ID))
p3
monthOrder <- c('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')
BaseModel$Month <- factor(format(BaseModel$Date, "%b"), levels = monthOrder)
BaseModel$Year <- factor(format(BaseModel$Date, "%Y"))
ggplot(BaseModel, aes(Month, TPLoad)) + geom_boxplot(data=subset(BaseModel,Station_ID=="LSPC188PERO")) +
geom_boxplot(data=subset(BaseModel,Station_ID=="HARPETH_62")) +
stat_boxplot(geom ='errorbar') + ggtitle("TP Load (lbs/day)")
ggplot(BaseModel,aes(Month,TPLoad)) + geom_bar(stat="identity") + ggtitle("TP Load (lbs/month)")
Natural$Month <- factor(format(Natural$Date, "%b"), levels = monthOrder)
Natural$Year <- factor(format(Natural$Date, "%Y"))
ggplot(Natural, aes(Month, TPLoad)) + geom_boxplot(data=subset(Natural,Station_ID=="LSPC188PERO")) + stat_boxplot(geom ='errorbar') + ggtitle("Natural Model - TP Load (lbs/day)")
Base_NoFrank1$Month <- factor(format(Base_NoFrank1$Date, "%b"), levels = monthOrder)
Base_NoFrank1$Year <- factor(format(Base_NoFrank1$Date, "%Y"))
ggplot(Base_NoFrank1, aes(Month, TPLoad)) + geom_boxplot(data=subset(Base_NoFrank1,Station_ID=="LSPC188PERO")) + stat_boxplot(geom ='errorbar') + ggtitle("Base_NoFrank1 Model - TP Load (lbs/day)")
Base_PS1$Month <- factor(format(Base_PS1$Date, "%b"), levels = monthOrder)
Base_PS1$Year <- factor(format(Base_PS1$Date, "%Y"))
ggplot(Base_PS1, aes(Month, TPLoad)) + geom_boxplot(data=subset(Base_PS1,Station_ID=="LSPC188PERO")) + stat_boxplot(geom ='errorbar') + ggtitle("Base_PS1 Model - TP Load (lbs/day)")
##################
#LSPC188_tpload <- subset(BaseModel, Station_ID==c("LSPC188PERO","HARPETH_62"), select=c(Station_ID, Year, Month, TPLoad))
#meanTPLoad_62 <- subset(BaseModel, Station_ID=="HARPETH_62", select=c(Station_ID, Year, Month, TPLoad))
#meanTPLoad_188 <- subset(BaseModel, Station_ID=="LSPC188PERO", select=c(Station_ID, Year, Month, TPLoad))
#library(plyr)
#tpload62<-ddply(meanTPLoad_62, c("Month","Station_ID"), summarise, x = mean(TPLoad))
#meanTPLoad_62 <- join(meanTPLoad_62, tpload62, match="all")
#ggplot(LSPC188_tpload,aes(Year,TPLoad,colour=Station_ID)) +
#geom_point(data=LSPC188_tpload,size=I(2),alpha=I(0.6)) +
#geom_line(data=meanTPLoad_62, aes(Month,x), size=I(1.5),alpha=I(0.6)) +
##geom_line(data=mean(meanTPLoad_62$TPLoad),size=I(1.5),alpha=I(0.4)) +
#theme_grey(base_size=15) +
#theme(legend.title = element_blank(), legend.position=c(.85,.85), axis.title.y=element_blank(),axis.text.x=element_blank()) +
#ggtitle("TP Load (lbs/day)") + facet_grid(. ~ Month) +
#xlab(paste("Years: 2011 to 2019"))
#theme_set(theme_classic())
#BaseModel$Date.ts<-as.ts(BaseModel$Date)
# Subset data
#Base_LSPC188 <- window(subset(BaseModel,Station_ID=="LSPC188PERO"), start=c(2012, 1), end=c(2019, 8)) # subset a smaller timewindow
# Plot
#ggseasonplot(subset(BaseModel,Station_ID=="LSPC188PERO"), x=BaseModel$Date.ts) + labs(title="Seasonal plot: International Airline Passengers")
#ggseasonplot(subset(BaseModel,Station_ID=="LSPC188PERO")) + labs(title="Seasonal plot: Air temperatures at Nottingham Castle")
p <- ggplot(data=BaseModel1, aes(x=Date, y=DO, color=Station_ID)) +
geom_line(data=subset(BaseModel1,Station_ID=="LSPC188PERO")) +
xlab("Year") +
geom_line(data=subset(BaseModel1, Station_ID==sites))
p
p <- ggplot(data=Base_PS1, aes(x=Date, y=DO, color=Station_ID)) +
geom_line(data=subset(Base_PS1,Station_ID=="LSPC188PERO")) +
xlab("Year") +
geom_line(data=subset(Base_PS1, Station_ID==sites))
p
p <- ggplot(data=Natural1, aes(x=Date, y=DO, color=Station_ID)) +
geom_line(data=subset(Natural1,Station_ID=="LSPC188PERO")) +
xlab("Year") +
geom_line(data=subset(Natural1, Station_ID==sites))
p
LSPC188.base<-subset(BaseModel1,Station_ID=="LSPC188PERO")
dy.tpload <- xts(x = LSPC188.base$TPLoad, order.by = LSPC188.base$Date)
LSPC188.natural<-subset(Natural1,Station_ID=="LSPC188PERO")
dy.Nattpload <- xts(x = LSPC188.natural$TPLoad, order.by = LSPC188.natural$Date)
LSPC188.tpload<-cbind(dy.tpload,dy.Nattpload)
LSPC188.tpload <- na.locf(LSPC188.tpload)
p <- dygraph(LSPC188.tpload, main="TP Load at LSPC 188 - Below Frank", ylab="TP Load (lbs/day)") %>%
dySeries("dy.tpload", drawPoints = TRUE, strokePattern="dashed", color="red") %>%
dySeries("dy.Nattpload", drawPoints= T, color="blue") %>%
dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors = RColorBrewer::brewer.pal(3, "Set2")) %>%
dyRangeSelector() %>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE) %>%
dyRoller(rollPeriod = 1)
pLSPC192.base<-subset(BaseModel1,Station_ID=="LSPC192PERO")
dy.DO <- xts(x = LSPC192.base$DO, order.by = LSPC192.base$Date)
LSPC192.natural<-subset(Natural1,Station_ID=="LSPC192PERO")
dy.NatDO <- xts(x = LSPC192.natural$DO, order.by = LSPC192.natural$Date)
LSPC192.Base_PS<-subset(Base_PS1,Station_ID=="LSPC192PERO")
dy.Base_PSDO <- xts(x = LSPC192.Base_PS$DO, order.by = LSPC192.Base_PS$Date)
LSPC192.Cont<-subset(Cont.w,Station_ID==c("USGS_03433500","HARPE062"))
dy.ContDO <- xts(x = LSPC192.Cont$DO, order.by = LSPC192.Cont$Date)
LSPC192.DO<-cbind(dy.DO,dy.NatDO,dy.Base_PSDO,dy.ContDO)
#LSPC192.DO <- na.locf(LSPC192.DO)
p1 <- dygraph(LSPC192.DO, main="Dissolved Oxygen at LSPC 192 - Hwy 100", ylab="DO (mg/L)") %>%
dySeries("dy.DO", drawPoints = TRUE, strokePattern="dashed", color="red") %>%
dySeries("dy.NatDO", drawPoints= T, color="blue") %>%
dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors = RColorBrewer::brewer.pal(4, "Set1")) %>%
dyRangeSelector() %>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE) %>%
dyRoller(rollPeriod = 1)
p1LSPC188.base<-subset(BaseModel1,Station_ID=="LSPC188PERO")
dy.DO <- xts(x = LSPC188.base$DO, order.by = LSPC188.base$Date)
LSPC188.natural<-subset(Natural1,Station_ID=="LSPC188PERO")
dy.NatDO <- xts(x = LSPC188.natural$DO, order.by = LSPC188.natural$Date)
LSPC188.Base_PS<-subset(Base_PS1,Station_ID=="LSPC188PERO")
dy.Base_PSDO <- xts(x = LSPC188.Base_PS$DO, order.by = LSPC188.Base_PS$Date)
LSPC188.Cont<-subset(Cont.w,Station_ID==c("USGS_03432400","USGS_03432350"))
dy.ContDO <- xts(x = LSPC188.Cont$DO, order.by = LSPC188.Cont$Date)
LSPC188.Grab<-subset(Grab.w,Station_ID==c("Frank_Down","Frank_CottonLn", "Frank_Eff", "Franklin_Site_2", "Franklin_Site_3"))## Warning in Station_ID == c("Frank_Down", "Frank_CottonLn", "Frank_Eff", : longer
## object length is not a multiple of shorter object lengthdy.GrabDO <- xts(x = LSPC188.Grab$DO, order.by = LSPC188.Grab$Date)
LSPC188.DO<-cbind(dy.DO,dy.NatDO,dy.Base_PSDO,dy.ContDO,dy.GrabDO)
#LSPC188.DO <- na.locf(LSPC188.DO)
p2 <- dygraph(LSPC188.DO, main="Dissolved Oxygen at LSPC 188 - Below Franklin STP", ylab="DO (mg/L)") %>%
dySeries("dy.DO", drawPoints = TRUE, strokePattern="dashed", color="red") %>%
dySeries("dy.NatDO", drawPoints= T, color="blue") %>%
dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors = RColorBrewer::brewer.pal(5, "Set1")) %>%
dyRangeSelector() %>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE) %>%
dyRoller(rollPeriod = 1)
p2HARPETH_62.base<-subset(BaseModel1,Station_ID=="HARPETH_62")
dy.DO <- xts(x = HARPETH_62.base$DO, order.by = HARPETH_62.base$Date)
HARPETH_62.natural<-subset(Natural1,Station_ID=="HARPETH_62")
dy.NatDO <- xts(x = HARPETH_62.natural$DO, order.by = HARPETH_62.natural$Date)
HARPETH_62.Base_PS<-subset(Base_PS1,Station_ID=="HARPETH_62")
dy.Base_PSDO <- xts(x = HARPETH_62.Base_PS$DO, order.by = HARPETH_62.Base_PS$Date)
HARPETH_62.Cont<-subset(Cont.w,Station_ID==c("usgs_03432350","USGS_0343233905"))
dy.ContDO <- xts(x = HARPETH_62.Cont$DO, order.by = HARPETH_62.Cont$Date)
HARPETH_62.Grab<-subset(Grab.w,Station_ID==c("Frank_Up","Franklin_Site_1"))## Warning in Station_ID == c("Frank_Up", "Franklin_Site_1"): longer object length
## is not a multiple of shorter object lengthdy.GrabDO <- xts(x = HARPETH_62.Grab$DO, order.by = HARPETH_62.Grab$Date)
HARPETH_62.DO<-cbind(dy.DO,dy.NatDO,dy.Base_PSDO,dy.ContDO,dy.GrabDO)
#HARPETH_62.DO <- na.locf(HARPETH_62.DO)
p3 <- dygraph(HARPETH_62.DO, main="Dissolved Oxygen at Harpeth 62 - Upstream of Franklin STP", ylab="DO (mg/L)") %>%
dySeries("dy.DO", drawPoints = TRUE, strokePattern="dashed", color="red") %>%
dySeries("dy.NatDO", drawPoints= T, color="blue") %>%
dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors = RColorBrewer::brewer.pal(5, "Set1")) %>%
dyRangeSelector() %>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE) %>%
dyRoller(rollPeriod = 1)
p3JONES_167.base<-subset(BaseModel1,Station_ID=="JONES_167")
dy.DO <- xts(x = JONES_167.base$DO, order.by = JONES_167.base$Date)
JONES_167.natural<-subset(Natural1,Station_ID=="JONES_167")
dy.NatDO <- xts(x = JONES_167.natural$DO, order.by = JONES_167.natural$Date)
JONES_167.Base_PS<-subset(Base_PS1,Station_ID=="JONES_167")
dy.Base_PSDO <- xts(x = JONES_167.Base_PS$DO, order.by = JONES_167.Base_PS$Date)
#JONES_167.Cont<-subset(Cont.w,Station_ID==c("usgs_03432350","USGS_0343233905"))
#dy.ContDO <- xts(x = JONES_167.Cont$DO, order.by = JONES_167.Cont$Date)
#JONES_167.Grab<-subset(Grab.w,Station_ID==c("Frank_Up","Franklin_Site_1"))
#dy.GrabDO <- xts(x = JONES_167.Grab$DO, order.by = JONES_167.Grab$Date)
JONES_167.DO<-cbind(dy.DO,dy.NatDO,dy.Base_PSDO)
#JONES_167.DO <- na.locf(JONES_167.DO)
p4 <- dygraph(JONES_167.DO, main="Dissolved Oxygen at JONES_167 - Mouth of Jones Creek", ylab="DO (mg/L)") %>%
dySeries("dy.DO", drawPoints = TRUE, strokePattern="dashed", color="red") %>%
dySeries("dy.NatDO", drawPoints= T, color="blue") %>%
dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors = RColorBrewer::brewer.pal(5, "Set1")) %>%
dyRangeSelector() %>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE) %>%
dyRoller(rollPeriod = 1)
p4LSPC183PERO.base<-subset(BaseModel1,Station_ID=="LSPC183PERO")
dy.DO <- xts(x = LSPC183PERO.base$DO, order.by = LSPC183PERO.base$Date)
LSPC183PERO.natural<-subset(Natural1,Station_ID=="LSPC183PERO")
dy.NatDO <- xts(x = LSPC183PERO.natural$DO, order.by = LSPC183PERO.natural$Date)
LSPC183PERO.Base_PS<-subset(Base_PS1,Station_ID=="LSPC183PERO")
dy.Base_PSDO <- xts(x = LSPC183PERO.Base_PS$DO, order.by = LSPC183PERO.Base_PS$Date)
#LSPC183PERO.Cont<-subset(Cont.w,Station_ID==c("usgs_03432350","USGS_0343233905"))
#dy.ContDO <- xts(x = LSPC183PERO.Cont$DO, order.by = LSPC183PERO.Cont$Date)
#LSPC183PERO.Grab<-subset(Grab.w,Station_ID==c("Frank_Up","Franklin_Site_1"))
#dy.GrabDO <- xts(x = LSPC183PERO.Grab$DO, order.by = LSPC183PERO.Grab$Date)
LSPC183PERO.DO<-cbind(dy.DO,dy.NatDO,dy.Base_PSDO)
#LSPC183PERO.DO <- na.locf(LSPC183PERO.DO)
p4 <- dygraph(LSPC183PERO.DO, main="Dissolved Oxygen at LSPC183PERO - Headwaters", ylab="DO (mg/L)") %>%
dySeries("dy.DO", drawPoints = TRUE, strokePattern="dashed", color="red") %>%
dySeries("dy.NatDO", drawPoints= T, color="blue") %>%
dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors = RColorBrewer::brewer.pal(5, "Set1")) %>%
dyRangeSelector() %>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE) %>%
dyRoller(rollPeriod = 1)
p4S_HARPETH_124.base<-subset(BaseModel1,Station_ID=="S_HARPETH_124")
dy.DO <- xts(x = S_HARPETH_124.base$DO, order.by = S_HARPETH_124.base$Date)
S_HARPETH_124.natural<-subset(Natural1,Station_ID=="S_HARPETH_124")
dy.NatDO <- xts(x = S_HARPETH_124.natural$DO, order.by = S_HARPETH_124.natural$Date)
S_HARPETH_124.Base_PS<-subset(Base_PS1,Station_ID=="S_HARPETH_124")
dy.Base_PSDO <- xts(x = S_HARPETH_124.Base_PS$DO, order.by = S_HARPETH_124.Base_PS$Date)
#S_HARPETH_124.Cont<-subset(Cont.w,Station_ID==c("usgs_03432350","USGS_0343233905"))
#dy.ContDO <- xts(x = S_HARPETH_124.Cont$DO, order.by = S_HARPETH_124.Cont$Date)
#S_HARPETH_124.Grab<-subset(Grab.w,Station_ID==c("Frank_Up","Franklin_Site_1"))
#dy.GrabDO <- xts(x = S_HARPETH_124.Grab$DO, order.by = S_HARPETH_124.Grab$Date)
S_HARPETH_124.DO<-cbind(dy.DO,dy.NatDO,dy.Base_PSDO)
#S_HARPETH_124.DO <- na.locf(S_HARPETH_124.DO)
p5 <- dygraph(S_HARPETH_124.DO, main="Dissolved Oxygen at S_HARPETH_124 - Mouth of South Harpeth", ylab="DO (mg/L)") %>%
dySeries("dy.DO", drawPoints = TRUE, strokePattern="dashed", color="red") %>%
dySeries("dy.NatDO", drawPoints= T, color="blue") %>%
dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors = RColorBrewer::brewer.pal(5, "Set1")) %>%
dyRangeSelector() %>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE) %>%
dyRoller(rollPeriod = 1)
p5DF <- melt(BaseModel, id=c("Date","Station_ID"))
ggplot(BaseModel, aes(x = TP, col = Station_ID))+
stat_ecdf(lwd = 1.2)+
geom_hline(yintercept=c(.95,0.75), color="black")+
xlim(0,1.2)+
geom_vline(xintercept = c(0.62,0.34))+labs(title="Base Model - EPA",
x ="TP (mg/L)", y = "Probability of Occurence")
ggplot(Natural, aes(x=TP, col=Station_ID))+
stat_ecdf(lwd=1.2)+
geom_hline(yintercept=c(.95,0.75), color="black")+
xlim(0,1.2)+labs(title="Model - Natural",
x ="TP (mg/L)", y = "Probability of Occurence")
ggplot(Base_PS, aes(x=TP, col=Station_ID))+
stat_ecdf(lwd=1.2)+
geom_hline(yintercept=c(.95,0.75), color="black")+
xlim(0,1.2)+labs(title="Base_PS",
x ="TP (mg/L)", y = "Probability of Occurence")
TP concentrations and NP ratios visualized with cumulative distribution frequency
ggplot(BaseModel, aes(x = NP, col = Station_ID))+
stat_ecdf(lwd = 1.2)+
geom_hline(yintercept=c(.95,0.75), color="black")+
xlim(0,10)+
geom_vline(xintercept = c(5,3.1))+labs(title="Base Model - EPA",
x ="NP Ratios", y = "Probability of Occurence")
ggplot(Natural, aes(x=NP, col=Station_ID))+
stat_ecdf(lwd=1.2)+
geom_hline(yintercept=c(.95,0.75), color="black")+
xlim(0,10)+labs(title="Model - Natural",
x ="NP Ratios", y = "Probability of Occurence")
LSPC188PERO.NoFrank<-subset(Base_NoFrank1,Station_ID=="LSPC188PERO")
dy.NoFrankTPLoad <- xts(x = LSPC188PERO.NoFrank$TPLoad, order.by = LSPC188PERO.NoFrank$Date)
LSPC188PERO.base<-subset(BaseModel1,Station_ID=="LSPC188PERO")
dy.TPLoad <- xts(x = LSPC188PERO.base$TPLoad, order.by = LSPC188PERO.base$Date)
LSPC188PERO.natural<-subset(Natural1,Station_ID=="LSPC188PERO")
dy.NatTPLoad <- xts(x = LSPC188PERO.natural$TPLoad, order.by = LSPC188PERO.natural$Date)
LSPC188PERO.Base_PS<-subset(Base_PS1,Station_ID=="LSPC188PERO")
dy.Base_PSTPLoad <- xts(x = LSPC188PERO.Base_PS$TPLoad, order.by = LSPC188PERO.Base_PS$Date)
LSPC188PERO.FrankPermit<-subset(Base_NoFrank1,Station_ID=="LSPC188PERO")
dy.FrankPermitTPLoad <- xts(x = LSPC188PERO.FrankPermit$TPLoad+174.5, order.by = LSPC188PERO.FrankPermit$Date)
#LSPC188PERO.Cont<-subset(Cont.w,Station_ID==c("usgs_03432350","USGS_0343233905"))
#dy.ContTPLoad <- xts(x = LSPC188PERO.Cont$TPLoad, order.by = LSPC188PERO.Cont$Date)
#LSPC188PERO.Grab<-subset(Grab.w,Station_ID==c("Frank_Up","Franklin_Site_1"))
#dy.GrabTPLoad <- xts(x = LSPC188PERO.Grab$TPLoad, order.by = LSPC188PERO.Grab$Date)
LSPC188PERO.TPLoad<-cbind(dy.TPLoad,dy.NatTPLoad,dy.Base_PSTPLoad,dy.NoFrankTPLoad,dy.FrankPermitTPLoad)
LSPC188PERO.TPLoad <- na.locf(LSPC188PERO.TPLoad)
p5 <- dygraph(LSPC188PERO.TPLoad, main="TP Load at LSPC188PERO - Below Franklin", ylab="TPLoad (lbs/day)") %>%
dySeries("dy.TPLoad", drawPoints = TRUE, strokePattern="dashed", color="red") %>%
dySeries("dy.NatTPLoad", drawPoints= T, color="blue") %>%
dyOptions(labelsUTC = TRUE, fillGraph=TRUE, fillAlpha=0.1, drawGrid = FALSE, colors = RColorBrewer::brewer.pal(5, "Set1")) %>%
dyRangeSelector() %>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(highlightCircleSize = 5, highlightSeriesBackgroundAlpha = 0.2, hideOnMouseOut = FALSE) %>%
dyRoller(rollPeriod = 1)
p5FrankPermitTPLoad = Permit limit 174 lbs/day added to No Franklin Model in order to represent TP Load in Harpeth with addition of Franklin Permit Limits
a<-subset(NPDES, Station_ID=="TN0020460")
b<-subset(NPDES, Station_ID=="TN0027278")
c<-subset(NPDES, Station_ID=="TN0028827")
d<-subset(NPDES, Station_ID=="TN0029718")
e<-subset(NPDES, Station_ID=="TN0057789")
f<-subset(NPDES, Station_ID=="TN0057827")
g<-subset(NPDES, Station_ID=="TN0057835")
h<-subset(NPDES, Station_ID=="TN0059790")
i<-subset(NPDES, Station_ID=="TN0060216")
j<-subset(NPDES, Station_ID=="TN0062332")
k<-subset(NPDES, Station_ID=="TN0064297")
l<-subset(NPDES, Station_ID=="TN0066958")
m<-subset(NPDES, Station_ID=="TN0067164")
n<-subset(NPDES, Station_ID=="TN0067873")
o<-subset(BaseModel, Station_ID=="HARPETH_1")
#c$TPLoad.csum<-c$TPLoad*0.00220462*NPDES$FLOW*86400
#MonthlyLoad <- function(x,y){
# return(x*y*0.00220462*86400*30)
#}
#add_one_if <- function(vector, x_id, y_id, run_on_id){
# if(vector[run_on_id]){
# return(add_one(vector,x_id))}
# else{
# return(vector[x_id])
# }
#}
#test$y <- apply(test, 1, add_one_if, x_id = 1, y_id = 2, run_on_id = 3)
#NPDES.list<-c("a","b","c","d","e","f","g","h","i","j","k","l","m","n")
#for (i in NPDES.list) {
#i$Date2=dplyr::lead(i$Date)
#i$Date.dif<-difftime(i$Date2,i$Date)
#}
#NPDES$TPLoad.csum <- ave(NPDES$TPLoad, NPDES$Station_ID, FUN=cumsum)
#NPDES$NP<-NPDES$TN/NPDES$TP
#NPDES1<-NPDES
#PS.TPload<-max(a$TPLoad.csum)+max(b$TPLoad.csum)+max(c$TPLoad.csum)+max(d$TPLoad.csum)+max(e$TPLoad.csum)+max(f$TPLoad.csum)+max(g$TPLoad.csum)+max(h$TPLoad.csum)+max(i$TPLoad.csum)+max(j$TPLoad.csum)+max(k$TPLoad.csum)+max(l$TPLoad.csum)+max(m$TPLoad.csum)+max(n$TPLoad.csum)
#PS.TPload #Cumulative TP Load between Jan 2011 and Dec 2019 of all combined Point Sources from NPDES permits
#max(o$TPLoad.csum) #Cumulative TP Load between Jan 2011 and Dec 2019 from HARPETH mouth (HARPETH_1)
#PS.TPload/max(o$TPLoad.csum)
#PS.TPload<-data.frame(c$Date,PS.TPload)
#colnames(PS.TPload)[1] <- "Date"
#colnames(PS.TPload)[2] <- "TPLoad.csum"
#p <- ggplot(data=PS.TPload, aes(x=Date, y=log(TPLoad.csum))) +
# geom_line() +
# xlab("Year") +
# geom_line(data=subset(BaseModel, Station_ID=="HARPETH_1"))
#p